Image studies of a patient may be captured by a plurality of different cross-sectional imaging modalities, such as computed tomography (CT) imaging, magnetic resonance (MR) imaging and the like. The image study may include a plurality of image slices captured at different locations along a patient's body. For example, a patient lying on a table may undergo an image scan with image slices captured at each of a plurality of discrete locations. As a more particular example, the image study of the abdomen of a patient may include a plurality of image slices including image slices of the upper abdomen, images slices of the mid-abdomen and image slices of the lower abdomen. As another example, the image study of the head of a patient may begin with image slices at the top of the patient's head and conclude with image slices of the patient's neck.
While each image slice is two dimensional, an image study formed of a plurality of image slices provides a three-dimensional volumetric view of the patient and may be analyzed by a physician or other health care professional in order to assess the condition of the patient, such as for purposes of diagnosis or the determination of the effectiveness of a treatment. While image studies comprised of a plurality of image slices are advantageous in regards to the wealth of information that such image studies provide, this same wealth of information may introduce difficulties or inefficiencies for a physician or other health care professional who is reviewing an image study. In this regard, a physician or other health care professional may have to review a substantial number of the image slices of an image study in order to find the subset of image slices that are most relevant to the evaluation of the patient. This review may be time consuming and taxing as the physician or other health care professional must attentively review numerous image slices to identify those of most interest.
In order to facilitate the identification of the subset of image slices that is of most interest, techniques for fiducial or anatomic landmark detection have been developed. These techniques review the image slices captured by cross-sectional imaging modalities, such as CT and MR imaging modalities, in order to identify particular features, termed fiduciary markers or anatomical landmarks, within the image study. Once the particular features have been identified within the image study, a physician or other health care professional may more efficiently review the image study by focusing their review upon a subset of image slices that are located in a known relationship to the identified features. For example, an image study of the torso of a patient may be subjected to fiducial or anatomic landmark detection techniques which serve to identify the location of the chest and the abdomen within the image study. Thus, a physician reviewing the image study may more quickly locate the image slices that represent the stomach, based upon the relative location of the stomach (which resides within the abdomen) with respect to the lungs (which reside in the chest), so as to facilitate their review of the relevant portion of the image study.
Fiducial or anatomic landmark detection techniques utilize three-dimensional volumetric image processing and/or analysis techniques in order to analyze the image slices of an image study. As such, fiducial or anatomic landmark detection techniques are relatively complex and employ substantial computing resources in order to detect the desired fiducial or anatomic markers. In this regard, the complexity of the fiducial or anatomic landmark detection techniques may increase depending upon the nature of the fiducial or anatomic markers with many image slices being required to be subjected to the image processing and analysis techniques. Fiducial or anatomic landmark detection techniques rely upon image analysis of the picture elements (pixels) of one or more image slices of an image study. Thus, these techniques not only require access to the values of the picture elements of the different image slices, but also generally require the processing of substantial amounts of data, such as the analysis of large numbers of pixel intensity values for model matching or other purposes. Thus, these fiducial or anatomic landmark detection techniques require substantial processing power and utilize relatively large amounts of memory. However, as a result of the three-dimensional volumetric image processing and analysis performed by such fiducial or anatomic landmark detection techniques, the fiducial or anatomic markers may be identified with reasonable precision, thereby effectively guiding the review of an image study by a physician or other health care professional.